Sparse representation based pansharpening using trained dictionary,” Geos

Abstract-Sparse representation has been used to fuse highresolution panchromatic (HRP) and low-resolution multispectral (LRM) images. However, the approach faces the difficulty that the dictionary is generated from the high-resolution multispectral (HRM) images, which are unknown. In this letter, a two-step method is proposed to train the dictionary from the HRP and LRM images. In the first step, coarse HRM images are obtained by additive wavelet fusion method. The initial dictionary is composed of randomly sampled patches from the coarse HRM images. In the second step, a linear constraint K-SVD method is designed to train the dictionary to improve its representation ability. Experimental results using QuickBird and IKONOS data indicate that the trained dictionary yields comparable fusion products with raw-patch-dictionary sampled from HRM images

Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware

Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net

Hyperspectral imaging can help better understand the characteristics of different materials, compared with traditional image systems. However, only high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS) images can generally be captured at video rate in practice. In this paper, we propose a model-based deep learning approach for merging an HrMS and LrHS images to generate a high-resolution hyperspectral (HrHS) image. In specific, we construct a novel MS/HS fusion model which takes the observation models of low-resolution images and the low-rankness knowledge along the spectral mode of HrHS image into consideration. Then we design an iterative algorithm to solve the model by exploiting the proximal gradient method. And then, by unfolding the designed algorithm, we construct a deep network, called MS/HS Fusion Net, with learning the proximal operators and model parameters by convolutional neural networks. Experimental results on simulated and real data substantiate the superiority of our method both visually and quantitatively as compared with state-of-the-art methods along this line of research.Comment: 10 pages, 7 figure

Hyperspectral and Multispectral Image Fusion using Optimized Twin Dictionaries

Spectral or spatial dictionary has been widely used in fusing low-spatial-resolution hyperspectral (LH) images and high-spatial-resolution multispectral (HM) images. However, only using spectral dictionary is insufficient for preserving spatial information, and vice versa. To address this problem, a new LH and HM image fusion method termed OTD using optimized twin dictionaries is proposed in this paper. The fusion problem of OTD is formulated analytically in the framework of sparse representation, as an optimization of twin spectral-spatial dictionaries and their corresponding sparse coefficients. More specifically, the spectral dictionary representing the generalized spectrums and its spectral sparse coefficients are optimized by utilizing the observed LH and HM images in the spectral domain; and the spatial dictionary representing the spatial information and its spatial sparse coefficients are optimized by modeling the rest of high-frequency information in the spatial domain. In addition, without non-negative constraints, the alternating direction methods of multipliers (ADMM) are employed to implement the above optimization process. Comparison results with the related state-of-the-art fusion methods on various datasets demonstrate that our proposed OTD method achieves a better fusion performance in both spatial and spectral domains

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Remote sensing image fusion via compressive sensing

In this paper, we propose a compressive sensing-based method to pan-sharpen the low-resolution multispectral (LRM) data, with the help of high-resolution panchromatic (HRP) data. In order to successfully implement the compressive sensing theory in pan-sharpening, two requirements should be satisfied: (i) forming a comprehensive dictionary in which the estimated coefficient vectors are sparse; and (ii) there is no correlation between the constructed dictionary and the measurement matrix. To fulfill these, we propose two novel strategies. The first is to construct a dictionary that is trained with patches across different image scales. Patches at different scales or equivalently multiscale patches provide texture atoms without requiring any external database or any prior atoms. The redundancy of the dictionary is removed through K-singular value decomposition (K-SVD). Second, we design an iterative l1-l2 minimization algorithm based on alternating direction method of multipliers (ADMM) to seek the sparse coefficient vectors. The proposed algorithm stacks missing high-resolution multispectral (HRM) data with the captured LRM data, so that the latter is used as a constraint for the estimation of the former during the process of seeking the representation coefficients. Three datasets are used to test the performance of the proposed method. A comparative study between the proposed method and several state-of-the-art ones shows its effectiveness in dealing with complex structures of remote sensing imagery